Detecting a signal quality decrease in a measurement system

ABSTRACT

Techniques for detecting a signal quality decrease are disclosed. A sensor or probe may be used to obtain a plethysmograph or photoplethysmograph (PPG) signal from a subject. A wavelet transform of the signal may be performed and a scalogram may be generated based at least in part on the wavelet transform. One or more characteristics of the scalogram may be determined. The determined characteristics may include, for example, energy values and energy structural characteristics in a pulse band, a mains hum band, and/or a noise band. Such characteristics may be analyzed to produce signal quality values and associated signal quality trends. One or more signal quality values and signal quality trends may be used to determine if a signal quality decrease has occurred or is likely to occur.

This application is a continuation of U.S. patent application Ser. No.12/242,204 filed on Sep. 30, 2008, which is incorporated by referenceherein in its entirety.

SUMMARY

The present disclosure relates to signal processing and, moreparticularly, the present disclosure relates to using characteristics ofone or more wavelet scalograms of a signal, such as aphotoplethysmograph (PPG) signal, to determine if a signal qualitydecrease has occurred or is likely to occur in a system, such as a pulseoximetry system.

In an embodiment, a pulse oximetry system is used to measure and analyzephysiological characteristics of a patient. A signal quality decreasemay occur when the target stimulus (e.g., a patient fingertip, toe,forehead, earlobe, or foot) is no longer adequately reflected inmeasurement of the PPG signal. Possible causes of such a signal qualitydecrease include motion artifacts that may be caused by, for example,voluntary or involuntary respiration, eye movements, swallowing,yawning, cardiac motion, and/or general body movement of a patient, thesensor being accidentally dislodged, or the sensor or any constituentcomponent of the sensor being damaged or otherwise malfunctioning.

In an embodiment, a PPG signal is transformed using a continuous wavelettransform. Continuous wavelet transforms allow for the use of multiplewavelets that are each scaled in accordance with scales of interest of asignal such that smaller scale components of a signal are transformedusing wavelets scaled more compactly than wavelets used to extractlarger scale components of the signal. The window size of data to whicheach wavelet gets applied varies according to scale as well. Thus, ahigher resolution transform is possible using continuous waveletsrelative to discrete techniques.

In an embodiment, one or more scalograms may be obtained by processingthe wavelet transform. Each scalogram may represent the energy densityof the PPG signal, where a suitable scaling has been performed toemphasize certain scale values or ranges of interest for the analysis ofthe PPG signal. In addition, the scalogram may contain information onthe real part of the wavelet transform, the imaginary part of thewavelet transform, the phase of the wavelet transform, any othersuitable part of the wavelet transform, or any combination thereof.

In an embodiment, a set of one or more characteristics may be determinedby applying one or more time-windows to one or more scalograms of a PPGsignal. Time-windows may be continuous or discontinuous, and may be usedto isolate regions or scale bands of the one or more scalograms. Thecharacteristics that are determined may chosen based on a pre-existingknowledge of features that are expected in a scalogram before and aftera signal quality decrease event. For example, the characteristics thatare determined may include some or all of the following items: energylevels in a pulse band, energy levels in a mains hum band; energy levelsin a noise band, energy structure in the pulse band, energy structure inthe mains hum band, and energy structure in the noise band.

In an embodiment, a set of one or more characteristics derived from oneor more scalograms may be analyzed. An analysis may includecurve-fitting one or more plots of data related to the one or morecharacteristics. Plots of data may depict the average or maximum energyobserved in a given region of the scalogram as a function of time, andcurve-fitting may involve interpolating or least-squares fitting theplotted data. An analysis may include calculating one or moresignal-to-noise levels based on the data related to the one or morecharacteristics. The signal-to-noise levels may correspond to the ratioof average or maximm energy density in a suitable signal band, such asthe pulse band, to the average or maximum energy density in a suitablenon-signal band, such as the mains hum band or the noise band.

In an embodiment, one or more signal qualities and one or moreassociated signal quality trends may be determined based on an analysisof characteristics derived from one or more scalograms. Each signalquality value may be represented by a number from 0 to 100, where alarger number indicates a higher quality signal, and each signal qualitytrend may be represented by a number representing a rate of increase ora rate of decrease in the signal quality value versus time. One or moresignal qualities and one or more associated signal quality trends may becombined or weighed according to any suitable method to determine anoverall signal quality value and an associated overall signal qualitytrend value.

In an embodiment, an overall signal quality value and an associatedoverall signal quality trend value may be used to determine oranticipate the presence of a signal quality value decrease event. Asignal may be triggered if it is determined that a signal qualitydecrease event has occurred or that one is likely to occur. For example,the triggered signal may sound an alarm or display one or more on-screenmessages to alert a user of the signal quality decrease. If it isdetermined that the signal quality decrease event has not occurred, thena new portion of a scalogram may be analyzed.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

The above and other features of the present disclosure, its nature andvarious advantages will be more apparent upon consideration of thefollowing detailed description, taken in conjunction with theaccompanying drawings in which:

FIG. 1 shows an illustrative pulse oximetry system in accordance with anembodiment;

FIG. 2 is a block diagram of the illustrative pulse oximetry system ofFIG. 1 coupled to a patient in accordance with an embodiment;

FIGS. 3( a) and 3(b) show illustrative views of a scalogram derived froma PPG signal in accordance with an embodiment;

FIG. 3( c) shows an illustrative scalogram derived from a signalcontaining two pertinent components in accordance with an embodiment;

FIG. 3( d) shows an illustrative schematic of signals associated with aridge in FIG. 3( c) and illustrative schematics of a further waveletdecomposition of these newly derived signals in accordance with anembodiment;

FIGS. 3( e) and 3(f) are flow charts of illustrative steps involved inperforming an inverse continuous wavelet transform in accordance withembodiments;

FIG. 4 is a block diagram of an illustrative continuous waveletprocessing system in accordance with an embodiment;

FIG. 5 shows an illustrative plot of a PPG signal taken during a periodof decreasing signal quality in accordance with an embodiment;

FIG. 6 shows an illustrative scalogram derived from the PPG signal ofFIG. 5 in accordance with an embodiment;

FIG. 7 shows illustrative plots of an energy measure versus time derivedfrom the scalogram of FIG. 6 in accordance with an embodiment;

FIG. 8 shows illustrative plots of the signal-to-noise level versus timederived from FIG. 7 in accordance with an embodiment;

FIG. 9 shows an illustrative plot of a PPG signal taken prior to andduring a period which includes a motion artifact in accordance with anembodiment;

FIG. 10 shows an illustrative scalogram derived from the PPG signal ofFIG. 9 in accordance with an embodiment;

FIG. 11 is a flow chart of an illustrative process for determining andresponding to a decrease in signal quality in accordance with anembodiment;

FIG. 12 shows a flow chart of an illustrative process for determining aset of characteristics in accordance with FIG. 11 and in an embodiment.

FIG. 13 shows a flow chart of an illustrative process for analyzing theset of characteristics in accordance with FIG. 11 and in an embodiment.

DETAILED DESCRIPTION

An oximeter is a medical device that may determine the oxygen saturationof the blood. One common type of oximeter is a pulse oximeter, which mayindirectly measure the oxygen saturation of a patient's blood (asopposed to measuring oxygen saturation directly by analyzing a bloodsample taken from the patient) and changes in blood volume in the skin.Ancillary to the blood oxygen saturation measurement, pulse oximetersmay also be used to measure the pulse rate of the patient. Pulseoximeters typically measure and display various blood flowcharacteristics including, but not limited to, the oxygen saturation ofhemoglobin in arterial blood.

An oximeter may include a light sensor that is placed at a site on apatient, typically a fingertip, toe, forehead or earlobe, or in the caseof a neonate, across a foot. The oximeter may pass light using a lightsource through blood perfused tissue and photoelectrically sense theabsorption of light in the tissue. For example, the oximeter may measurethe intensity of light that is received at the light sensor as afunction of time. A signal representing light intensity versus time or amathematical manipulation of this signal (e.g., a scaled versionthereof, a log taken thereof, a scaled version of a log taken thereof,etc.) may be referred to as the photoplethysmograph (PPG) signal. Inaddition, the term “PPG signal,” as used herein, may also refer to anabsorption signal (i.e., representing the amount of light absorbed bythe tissue) or any suitable mathematical manipulation thereof. The lightintensity or the amount of light absorbed may then be used to calculatethe amount of the blood constituent (e.g., oxyhemoglobin) being measuredas well as the pulse rate and when each individual pulse occurs.

The light passed through the tissue is selected to be of one or morewavelengths that are absorbed by the blood in an amount representativeof the amount of the blood constituent present in the blood. The amountof light passed through the tissue varies in accordance with thechanging amount of blood constituent in the tissue and the related lightabsorption. Red and infrared wavelengths may be used because it has beenobserved that highly oxygenated blood will absorb relatively less redlight and more infrared light than blood with a lower oxygen saturation.By comparing the intensities of two wavelengths at different points inthe pulse cycle, it is possible to estimate the blood oxygen saturationof hemoglobin in arterial blood.

When the measured blood parameter is the oxygen saturation ofhemoglobin, a convenient starting point assumes a saturation calculationbased on Lambert-Beer's law. The following notation will be used herein:I(λ,t)=I _(o)(λ)exp(−(sβ _(o)(λ)+(1−s)β_(r)(λ))l(t))  (1)where:λ=wavelength;t=time;I=intensity of light detected;I_(o)=intensity of light transmitted;s=oxygen saturation;β_(o), β_(r)=empirically derived absorption coefficients; andl(t)=a combination of concentration and path length from emitter todetector as a function of time.

The traditional approach measures light absorption at two wavelengths(e.g., red and infrared (IR)), and then calculates saturation by solvingfor the “ratio of ratios” as follows.

1. First, the natural logarithm of (1) is taken (“log” will be used torepresent the natural logarithm) for IR and Redlog I=log I _(o)−(sβ _(o)+(1−s)β_(r))l  (2)2. (2) is then differentiated with respect to time

$\begin{matrix}{\frac{{\mathbb{d}\log}\mspace{14mu} I}{\mathbb{d}t} = {{- \left( {{s\;\beta_{0}} + {\left( {1 - s} \right)\beta_{r}}} \right)}\frac{\mathbb{d}l}{\mathbb{d}t}}} & (3)\end{matrix}$3. Red (3) is divided by IR (3)

$\begin{matrix}{\frac{{\mathbb{d}\log}\;{I\left( \lambda_{R} \right)}\text{/}{\mathbb{d}t}}{{\mathbb{d}\log}\;{I\left( \lambda_{IR} \right)}\text{/}{\mathbb{d}t}} = \frac{{s\;{\beta_{o}\left( \lambda_{R} \right)}} + {\left( {1 - s} \right){\beta_{r}\left( \lambda_{R} \right)}}}{{s\;{\beta_{o}\left( \lambda_{IR} \right)}} + {\left( {1 - s} \right){\beta_{r}\left( \lambda_{IR} \right)}}}} & (4)\end{matrix}$4. Solving for s

$s = \frac{{\frac{{\mathbb{d}\log}\;{I\left( \lambda_{IR} \right)}}{\mathbb{d}t}{\beta_{r}\left( \lambda_{R} \right)}} - {\frac{{\mathbb{d}\log}\;{I\left( \lambda_{R} \right)}}{\mathbb{d}t}{\beta_{r}\left( \lambda_{IR} \right)}}}{{\frac{{\mathbb{d}\log}\;{I\left( \lambda_{R} \right)}}{\mathbb{d}t}\left( {{\beta_{o}\left( \lambda_{IR} \right)} - {\beta_{r}\left( \lambda_{IR} \right)}} \right)} - {\frac{{\mathbb{d}\log}\;{I\left( \lambda_{IR} \right)}}{\mathbb{d}t}\left( {{\beta_{o}\left( \lambda_{R} \right)} - {\beta_{r}\left( \lambda_{R} \right)}} \right)}}$Note in discrete time

$\frac{{\mathbb{d}\log}\;{I\left( {\lambda,t} \right)}}{\mathbb{d}t} \simeq {{\log\;{I\left( {\lambda,t_{2}} \right)}} - {\log\;{I\left( {\lambda,t_{1}} \right)}}}$Using log A−log B=log A/B,

$\frac{{\mathbb{d}\log}\;{I\left( {\lambda,t} \right)}}{\mathbb{d}t} \simeq {\log\left( \frac{I\left( {t_{2},\lambda} \right)}{I\left( {t_{1},\lambda} \right)} \right)}$So, (4) can be rewritten as

$\begin{matrix}{{\frac{\frac{{\mathbb{d}\log}\;{I\left( \lambda_{R} \right)}}{\mathbb{d}t}}{\frac{{\mathbb{d}\log}\;{I\left( \lambda_{IR} \right)}}{\mathbb{d}t}} \simeq \frac{\log\left( \frac{I\left( {t_{1},\lambda_{R}} \right)}{I\left( {t_{2},\lambda_{R}} \right)} \right)}{\log\left( \frac{I\left( {t_{1},\lambda_{IR}} \right)}{I\left( {t_{2},\lambda_{IR}} \right)} \right)}} = R} & (5)\end{matrix}$where R represents the “ratio of ratios.” Solving (4) for s using (5)gives

$s = {\frac{{\beta_{r}\left( \lambda_{R} \right)} - {R\;{\beta_{r}\left( \lambda_{IR} \right)}}}{{R\left( {{\beta_{o}\left( \lambda_{IR} \right)} - {\beta_{r}\left( \lambda_{IR} \right)}} \right)} - {\beta_{o}\left( \lambda_{R} \right)} + {\beta_{r}\left( \lambda_{R} \right)}}.}$From (5), R can be calculated using two points (e.g., PPG maximum andminimum), or a family of points. One method using a family of pointsuses a modified version of (5). Using the relationship

$\begin{matrix}{\frac{{\mathbb{d}\log}\; I}{\mathbb{d}t} = \frac{{\mathbb{d}I}\text{/}{\mathbb{d}t}}{I}} & (6)\end{matrix}$now (5) becomes

$\begin{matrix}\begin{matrix}{\frac{\frac{{\mathbb{d}\log}\;{I\left( \lambda_{R} \right)}}{\mathbb{d}t}}{\frac{{\mathbb{d}\log}\;{I\left( \lambda_{IR} \right)}}{\mathbb{d}t}} \simeq \frac{\frac{{I\left( {t_{2},\lambda_{R}} \right)} - {I\left( {t_{1},\lambda_{R}} \right)}}{I\left( {t_{1},\lambda_{R}} \right)}}{\frac{{I\left( {t_{2},\lambda_{IR}} \right)} - {I\left( {t_{1},\lambda_{IR}} \right)}}{I\left( {t_{1},\lambda_{IR}} \right)}}} \\{= \frac{\left\lbrack {{I\left( {t_{2},\lambda_{R}} \right)} - {I\left( {t_{1},\lambda_{R}} \right)}} \right\rbrack{I\left( {t_{1},\lambda_{IR}} \right)}}{\left\lbrack {{I\left( {t_{2},\lambda_{IR}} \right)} - {I\left( {t_{1},\lambda_{IR}} \right)}} \right\rbrack{I\left( {t_{1},\lambda_{IR}} \right)}}} \\{= R}\end{matrix} & (7)\end{matrix}$which defines a cluster of points whose slope of y versus x will give Rwherex(t)=[I(t ₂,λ_(IR))−I(t ₁,λ_(IR))]I(t ₁,λ_(R))y(t)=[I(t ₂,λ_(R))−I(t ₁,λ_(R))]I(t ₁,λ_(IR))y(t)=Rx(t)  (8)

FIG. 1 is a perspective view of an embodiment of a pulse oximetry system10. System 10 may include a sensor 12 and a pulse oximetry monitor 14.Sensor 12 may include an emitter 16 for emitting light at two or morewavelengths into a patient's tissue. A detector 18 may also be providedin sensor 12 for detecting the light originally from emitter 16 thatemanates from the patient's tissue after passing through the tissue.

According to another embodiment and as will be described, system 10 mayinclude a plurality of sensors forming a sensor array in lieu of singlesensor 12. Each of the sensors of the sensor array may be acomplementary metal oxide semiconductor (CMOS) sensor. Alternatively,each sensor of the array may be charged coupled device (CCD) sensor. Inanother embodiment, the sensor array may be made up of a combination ofCMOS and CCD sensors. The CCD sensor may comprise a photoactive regionand a transmission region for receiving and transmitting data whereasthe CMOS sensor may be made up of an integrated circuit having an arrayof pixel sensors. Each pixel may have a photodetector and an activeamplifier.

According to an embodiment, emitter 16 and detector 18 may be onopposite sides of a digit such as a finger or toe, in which case thelight that is emanating from the tissue has passed completely throughthe digit. In an embodiment, emitter 16 and detector 18 may be arrangedso that light from emitter 16 penetrates the tissue and is reflected bythe tissue into detector 18, such as a sensor designed to obtain pulseoximetry data from a patient's forehead.

In an embodiment, the sensor or sensor array may be connected to anddraw its power from monitor 14 as shown. In another embodiment, thesensor may be wirelessly connected to monitor 14 and include its ownbattery or similar power supply (not shown). Monitor 14 may beconfigured to calculate physiological parameters based at least in parton data received from sensor 12 relating to light emission anddetection. In an alternative embodiment, the calculations may beperformed on the monitoring device itself and the result of the oximetryreading may be passed to monitor 14. Further, monitor 14 may include adisplay 20 configured to display the physiological parameters or otherinformation about the system. In the embodiment shown, monitor 14 mayalso include a speaker 22 to provide an audible sound that may be usedin various other embodiments, such as for example, sounding an audiblealarm in the event that a patient's physiological parameters are notwithin a predefined normal range.

In an embodiment, sensor 12, or the sensor array, may be communicativelycoupled to monitor 14 via a cable 24. However, in other embodiments, awireless transmission device (not shown) or the like may be used insteadof or in addition to cable 24.

In the illustrated embodiment, pulse oximetry system 10 may also includea multi-parameter patient monitor 26. The monitor may be cathode raytube type, a flat panel display (as shown) such as a liquid crystaldisplay (LCD) or a plasma display, or any other type of monitor nowknown or later developed. Multi-parameter patient monitor 26 may beconfigured to calculate physiological parameters and to provide adisplay 28 for information from monitor 14 and from other medicalmonitoring devices or systems (not shown). For example, multiparameterpatient monitor 26 may be configured to display an estimate of apatient's blood oxygen saturation generated by pulse oximetry monitor 14(referred to as an “SpO₂” measurement), pulse rate information frommonitor 14 and blood pressure from a blood pressure monitor (not shown)on display 28.

Monitor 14 may be communicatively coupled to multi-parameter patientmonitor 26 via a cable 32 or 34 that is coupled to a sensor input portor a digital communications port, respectively and/or may communicatewirelessly (not shown). In addition, monitor 14 and/or multi-parameterpatient monitor 26 may be coupled to a network to enable the sharing ofinformation with servers or other workstations (not shown). Monitor 14may be powered by a battery (not shown) or by a conventional powersource such as a wall outlet.

FIG. 2 is a block diagram of a pulse oximetry system, such as pulseoximetry system 10 of FIG. 1, which may be coupled to a patient 40 inaccordance with an embodiment. Certain illustrative components of sensor12 and monitor 14 are illustrated in FIG. 2. Sensor 12 may includeemitter 16, detector 18, and encoder 42. In the embodiment shown,emitter 16 may be configured to emit at least two wavelengths of light(e.g., RED and IR) into a patient's tissue 40. Hence, emitter 16 mayinclude a RED light emitting light source such as RED light emittingdiode (LED) 44 and an IR light emitting light source such as IR LED 46for emitting light into the patient's tissue 40 at the wavelengths usedto calculate the patient's physiological parameters. In one embodiment,the RED wavelength may be between about 600 nm and about 700 nm, and theIR wavelength may be between about 800 nm and about 1000 nm. Inembodiments where a sensor array is used in place of single sensor, eachsensor may be configured to emit a single wavelength. For example, afirst sensor emits only a RED light while a second only emits an IRlight.

It will be understood that, as used herein, the term “light” may referto energy produced by radiative sources and may include one or more ofultrasound, radio, microwave, millimeter wave, infrared, visible,ultraviolet, gamma ray or X-ray electromagnetic radiation. As usedherein, light may also include any wavelength within the radio,microwave, infrared, visible, ultraviolet, or X-ray spectra, and thatany suitable wavelength of electromagnetic radiation may be appropriatefor use with the present techniques. Detector 18 may be chosen to bespecifically sensitive to the chosen targeted energy spectrum of theemitter 16.

In an embodiment, detector 18 may be configured to detect the intensityof light at the RED and IR wavelengths. Alternatively, each sensor inthe array may be configured to detect an intensity of a singlewavelength. In operation, light may enter detector 18 after passingthrough the patient's tissue 40. Detector 18 may convert the intensityof the received light into an electrical signal. The light intensity isdirectly related to the absorbance and/or reflectance of light in thetissue 40. That is, when more light at a certain wavelength is absorbedor reflected, less light of that wavelength is received from the tissueby the detector 18. After converting the received light to an electricalsignal, detector 18 may send the signal to monitor 14, wherephysiological parameters may be calculated based on the absorption ofthe RED and IR wavelengths in the patient's tissue 40. An example of adevice configured to perform such calculations is the Model N600x pulseoximeter available from Nellcor Puritan Bennett LLC.

In an embodiment, encoder 42 may contain information about sensor 12,such as what type of sensor it is (e.g., whether the sensor is intendedfor placement on a forehead or digit) and the wavelengths of lightemitted by emitter 16. This information may be used by monitor 14 toselect appropriate algorithms, lookup tables and/or calibrationcoefficients stored in monitor 14 for calculating the patient'sphysiological parameters.

Encoder 42 may contain information specific to patient 40, such as, forexample, the patient's age, weight, and diagnosis. This information mayallow monitor 14 to determine, for example, patient-specific thresholdranges in which the patient's physiological parameter measurementsshould fall and to enable or disable additional physiological parameteralgorithms. Encoder 42 may, for instance, be a coded resistor whichstores values corresponding to the type of sensor 12 or the type of eachsensor in the sensor array, the wavelengths of light emitted by emitter16 on each sensor of the sensor array, and/or the patient'scharacteristics. In another embodiment, encoder 42 may include a memoryon which one or more of the following information may be stored forcommunication to monitor 14: the type of the sensor 12; the wavelengthsof light emitted by emitter 16; the particular wavelength each sensor inthe sensor array is monitoring; a signal threshold for each sensor inthe sensor array; any other suitable information; or any combinationthereof.

In an embodiment, signals from detector 18 and encoder 42 may betransmitted to monitor 14. In the embodiment shown, monitor 14 mayinclude a general-purpose microprocessor 48 connected to an internal bus50. Microprocessor 48 may be adapted to execute software, which mayinclude an operating system and one or more applications, as part ofperforming the functions described herein. Also connected to bus 50 maybe a read-only memory (ROM) 52, a random access memory (RAM) 54, userinputs 56, display 20, and speaker 22.

RAM 54 and ROM 52 are illustrated by way of example, and not limitation.Any suitable computer-readable media may be used in the system for datastorage. Computer-readable media are capable of storing information thatcan be interpreted by microprocessor 48. This information may be data ormay take the form of computer-executable instructions, such as softwareapplications, that cause the microprocessor to perform certain functionsand/or computer-implemented methods. Depending on the embodiment, suchcomputer-readable media may include computer storage media andcommunication media. Computer storage media may include volatile andnon-volatile, removable and non-removable media implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules orother data. Computer storage media may include, but is not limited to,RAM, ROM, EPROM, EEPROM, flash memory or other solid state memorytechnology, CD-ROM, DVD, or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by components of the system.

In the embodiment shown, a time processing unit (TPU) 58 may providetiming control signals to a light drive circuitry 60, which may controlwhen emitter 16 is illuminated and multiplexed timing for the RED LED 44and the IR LED 46. TPU 58 may also control the gating-in of signals fromdetector 18 through an amplifier 62 and a switching circuit 64. Thesesignals are sampled at the proper time, depending upon which lightsource is illuminated. The received signal from detector 18 may bepassed through an amplifier 66, a low pass filter 68, and ananalog-to-digital converter 70. The digital data may then be stored in aqueued serial module (QSM) 72 (or buffer) for later downloading to RAM54 as QSM 72 fills up. In one embodiment, there may be multiple separateparallel paths having amplifier 66, filter 68, and A/D converter 70 formultiple light wavelengths or spectra received.

In an embodiment, microprocessor 48 may determine the patient'sphysiological parameters, such as SpO₂ and pulse rate, using variousalgorithms and/or look-up tables based on the value of the receivedsignals and/or data corresponding to the light received by detector 18.Signals corresponding to information about patient 40, and particularlyabout the intensity of light emanating from a patient's tissue overtime, may be transmitted from encoder 42 to a decoder 74. These signalsmay include, for example, encoded information relating to patientcharacteristics. Decoder 74 may translate these signals to enable themicroprocessor to determine the thresholds based on algorithms orlook-up tables stored in ROM 52. User inputs 56 may be used to enterinformation about the patient, such as age, weight, height, diagnosis,medications, treatments, and so forth. In an embodiment, display 20 mayexhibit a list of values which may generally apply to the patient, suchas, for example, age ranges or medication families, which the user mayselect using user inputs 56.

The optical signal through the tissue can be degraded by noise andmotion artifacts, among other sources. One source of noise is ambientlight that reaches the light detector. Another source of noise iselectromagnetic coupling from other electronic instruments. Movement ofthe patient also introduces noise and affects the signal. For example,the contact between the detector and the skin, or the emitter and theskin, can be temporarily disrupted when movement causes either to moveaway from the skin. In addition, because blood is a fluid, it respondsdifferently than the surrounding tissue to inertial effects, thusresulting in momentary changes in volume at the point to which theoximeter probe is attached.

Motion artifact can degrade a pulse oximetry signal relied upon by aphysician, without the physician's awareness. This is especially true ifthe monitoring of the patient is remote, the motion is too small to beobserved, or the doctor is watching the instrument or other parts of thepatient, and not the sensor site. Processing pulse oximetry (i.e., PPG)signals may involve operations that reduce the amount of noise presentin the signals or otherwise identify noise components in order toprevent them from affecting measurements of physiological parametersderived from the PPG signals.

It will be understood that the present disclosure is applicable to anysuitable signals and that PPG signals are used merely for illustrativepurposes. Those skilled in the art will recognize that the presentdisclosure has wide applicability to other signals including, but notlimited to other biosignals (e.g., electrocardiogram,electroencephalogram, electrogastrogram, electromyogram, heart ratesignals, pathological sounds, ultrasound, or any other suitablebiosignal), dynamic signals, non-destructive testing signals, conditionmonitoring signals, fluid signals, geophysical signals, astronomicalsignals, electrical signals, financial signals including financialindices, sound and speech signals, chemical signals, meteorologicalsignals including climate signals, and/or any other suitable signal,and/or any combination thereof.

In one embodiment, a PPG signal may be transformed using a continuouswavelet transform. Information derived from the transform of the PPGsignal (i.e., in wavelet space) may be used to provide measurements ofone or more physiological parameters.

The continuous wavelet transform of a signal x(t) in accordance with thepresent disclosure may be defined as

$\begin{matrix}{{T\left( {a,b} \right)} = {\frac{1}{\sqrt{a}}{\int_{- \infty}^{+ \infty}{{x(t)}{\psi^{*}\left( \frac{t - b}{a} \right)}\ {\mathbb{d}t}}}}} & (9)\end{matrix}$where ψ*(t) is the complex conjugate of the wavelet function ψ(t), a isthe dilation parameter of the wavelet and b is the location parameter ofthe wavelet. The transform given by equation (9) may be used toconstruct a representation of a signal on a transform surface. Thetransform may be regarded as a time-scale representation. Wavelets arecomposed of a range of frequencies, one of which may be denoted as thecharacteristic frequency of the wavelet, where the characteristicfrequency associated with the wavelet is inversely proportional to thescale a. One example of a characteristic frequency is the dominantfrequency. Each scale of a particular wavelet may have a differentcharacteristic frequency. The underlying mathematical detail requiredfor the implementation within a time-scale can be found, for example, inPaul S. Addison, The Illustrated Wavelet Transform Handbook (Taylor &Francis Group 2002), which is hereby incorporated by reference herein inits entirety.

The continuous wavelet transform decomposes a signal using wavelets,which are generally highly localized in time. The continuous wavelettransform may provide a higher resolution relative to discretetransforms, thus providing the ability to garner more information fromsignals than typical frequency transforms such as Fourier transforms (orany other spectral techniques) or discrete wavelet transforms.Continuous wavelet transforms allow for the use of a range of waveletswith scales spanning the scales of interest of a signal such that smallscale signal components correlate well with the smaller scale waveletsand thus manifest at high energies at smaller scales in the transform.Likewise, large scale signal components correlate well with the largerscale wavelets and thus manifest at high energies at larger scales inthe transform. Thus, components at different scales may be separated andextracted in the wavelet transform domain. Moreover, the use of acontinuous range of wavelets in scale and time position allows for ahigher resolution transform than is possible relative to discretetechniques.

In addition, transforms and operations that convert a signal or anyother type of data into a spectral (i.e., frequency) domain necessarilycreate a series of frequency transform values in a two-dimensionalcoordinate system where the two dimensions may be frequency and, forexample, amplitude. For example, any type of Fourier transform wouldgenerate such a two-dimensional spectrum. In contrast, wavelettransforms, such as continuous wavelet transforms, are required to bedefined in a three-dimensional coordinate system and generate a surfacewith dimensions of time, scale and, for example, amplitude. Hence,operations performed in a spectral domain cannot be performed in thewavelet domain; instead the wavelet surface must be transformed into aspectrum (i.e., by performing an inverse wavelet transform to convertthe wavelet surface into the time domain and then performing a spectraltransform from the time domain). Conversely, operations performed in thewavelet domain cannot be performed in the spectral domain; instead aspectrum must first be transformed into a wavelet surface (i.e., byperforming an inverse spectral transform to convert the spectral domaininto the time domain and then performing a wavelet transform from thetime domain). Nor does a cross-section of the three-dimensional waveletsurface along, for example, a particular point in time equate to afrequency spectrum upon which spectral-based techniques may be used. Atleast because wavelet space includes a time dimension, spectraltechniques and wavelet techniques are not interchangeable. It will beunderstood that converting a system that relies on spectral domainprocessing to one that relies on wavelet space processing would requiresignificant and fundamental modifications to the system in order toaccommodate the wavelet space processing (e.g., to derive arepresentative energy value for a signal or part of a signal requiresintegrating twice, across time and scale, in the wavelet domain while,conversely, one integration across frequency is required to derive arepresentative energy value from a spectral domain). As a furtherexample, to reconstruct a temporal signal requires integrating twice,across time and scale, in the wavelet domain while, conversely, oneintegration across frequency is required to derive a temporal signalfrom a spectral domain. It is well known in the art that, in addition toor as an alternative to amplitude, parameters such as energy density,modulus, phase, among others may all be generated using such transformsand that these parameters have distinctly different contexts andmeanings when defined in a two-dimensional frequency coordinate systemrather than a three-dimensional wavelet coordinate system. For example,the phase of a Fourier system is calculated with respect to a singleorigin for all frequencies while the phase for a wavelet system isunfolded into two dimensions with respect to a wavelet's location (oftenin time) and scale.

The energy density function of the wavelet transform, the scalogram, isdefined asS(a,b)=|T(a,b)|²  (10)where ‘∥’ is the modulus operator. The scalogram may be rescaled foruseful purposes. One common rescaling is defined as

$\begin{matrix}{{S_{R}\left( {a,b} \right)} = \frac{\left| {T\left( {a,b} \right)} \right|^{2}}{a}} & (11)\end{matrix}$and is useful for defining ridges in wavelet space when, for example,the Morlet wavelet is used. Ridges are defined as the locus of points oflocal maxima in the plane. Any reasonable definition of a ridge may beemployed in the method. Also included as a definition of a ridge hereinare paths displaced from the locus of the local maxima. A ridgeassociated with only the locus of points of local maxima in the planeare labeled a “maxima ridge”.

For implementations requiring fast numerical computation, the wavelettransform may be expressed as an approximation using Fourier transforms.Pursuant to the convolution theorem, because the wavelet transform isthe cross-correlation of the signal with the wavelet function, thewavelet transform may be approximated in terms of an inverse FFT of theproduct of the Fourier transform of the signal and the Fourier transformof the wavelet for each required a scale and then multiplying the resultby √{square root over (a)}.

In the discussion of the technology which follows herein, the“scalogram” may be taken to include all suitable forms of rescalingincluding, but not limited to, the original unscaled waveletrepresentation, linear rescaling, any power of the modulus of thewavelet transform, or any other suitable rescaling. In addition, forpurposes of clarity and conciseness, the term “scalogram” shall be takento mean the wavelet transform, T(a,b) itself, or any part thereof. Forexample, the real part of the wavelet transform, the imaginary part ofthe wavelet transform, the phase of the wavelet transform, any othersuitable part of the wavelet transform, or any combination thereof isintended to be conveyed by the term “scalogram”.

A scale, which may be interpreted as a representative temporal period,may be converted to a characteristic frequency of the wavelet function.The characteristic frequency associated with a wavelet of arbitrary ascale is given by

$\begin{matrix}{f = \frac{f_{c}}{a}} & (12)\end{matrix}$where f_(c), the characteristic frequency of the mother wavelet (i.e.,at a=1), becomes a scaling constant and f is the representative orcharacteristic frequency for the wavelet at arbitrary scale a.

Any suitable wavelet function may be used in connection with the presentdisclosure. One of the most commonly used complex wavelets, the Morletwavelet, is defined as:ψ(t)=π^(−1/4)(e ^(i2πf) ⁰ ^(t) −e ^(−(2πf) ⁰ ⁾ ² ^(/2))e ^(−t) ₂^(/2)  (13)where f₀ is the central frequency of the mother wavelet. The second termin the parenthesis is known as the correction term, as it corrects forthe non-zero mean of the complex sinusoid within the Gaussian window. Inpractice, it becomes negligible for values of f₀>>0 and can be ignored,in which case, the Morlet wavelet can be written in a simpler form as

$\begin{matrix}{{\psi(t)} = {\frac{1}{\pi^{1\text{/}4}}{\mathbb{e}}^{i\; 2\;\pi\; f_{0}t}{\mathbb{e}}^{{- t^{2}}\text{/}2}}} & (14)\end{matrix}$

This wavelet is a complex wave within a scaled Gaussian envelope. Whileboth definitions of the Morlet wavelet are included herein, the functionof equation (14) is not strictly a wavelet as it has a non-zero mean(i.e., the zero frequency term of its corresponding energy spectrum isnon-zero). However, it will be recognized by those skilled in the artthat equation (14) may be used in practice with f₀>>0 with minimal errorand is included (as well as other similar near wavelet functions) in thedefinition of a wavelet herein. A more detailed overview of theunderlying wavelet theory, including the definition of a waveletfunction, can be found in the general literature. Discussed herein ishow wavelet transform features may be extracted from the waveletdecomposition of signals. For example, wavelet decomposition of PPGsignals may be used to provide clinically useful information within amedical device.

Pertinent repeating features in a signal give rise to a time-scale bandin wavelet space or a rescaled wavelet space. For example, the pulsecomponent of a PPG signal produces a dominant band in wavelet space ator around the pulse frequency. FIGS. 3( a) and (b) show two views of anillustrative scalogram derived from a PPG signal, according to anembodiment. The figures show an example of the band caused by the pulsecomponent in such a signal. The pulse band is located between the dashedlines in the plot of FIG. 3( a). The band is formed from a series ofdominant coalescing features across the scalogram. This can be clearlyseen as a raised band across the transform surface in FIG. 3( b) locatedwithin the region of scales indicated by the arrow in the plot(corresponding to 60 beats per minute). The maxima of this band withrespect to scale is the ridge. The locus of the ridge is shown as ablack curve on top of the band in FIG. 3( b). By employing a suitablerescaling of the scalogram, such as that given in equation (11), theridges found in wavelet space may be related to the instantaneousfrequency of the signal. In this way, the pulse rate may be obtainedfrom the PPG signal. Instead of rescaling the scalogram, a suitablepredefined relationship between the scale obtained from the ridge on thewavelet surface and the actual pulse rate may also be used to determinethe pulse rate.

By mapping the time-scale coordinates of the pulse ridge onto thewavelet phase information gained through the wavelet transform,individual pulses may be captured. In this way, both times betweenindividual pulses and the timing of components within each pulse may bemonitored and used to detect heart beat anomalies, measure arterialsystem compliance, or perform any other suitable calculations ordiagnostics. Alternative definitions of a ridge may be employed.Alternative relationships between the ridge and the pulse frequency ofoccurrence may be employed.

As discussed above, pertinent repeating features in the signal give riseto a time-scale band in wavelet space or a rescaled wavelet space. For aperiodic signal, this band remains at a constant scale in the time-scaleplane. For many real signals, especially biological signals, the bandmay be non-stationary; varying in scale, amplitude, or both over time.FIG. 3( c) shows an illustrative schematic of a wavelet transform of asignal containing two pertinent components leading to two bands in thetransform space, according to an embodiment. These bands are labeledband A and band B on the three-dimensional schematic of the waveletsurface. In an embodiment, the band ridge is defined as the locus of thepeak values of these bands with respect to scale. For purposes ofdiscussion, it may be assumed that band B contains the signalinformation of interest. This will be referred to as the “primary band”.In addition, it may be assumed that the system from which the signaloriginates, and from which the transform is subsequently derived,exhibits some form of coupling between the signal components in band Aand band B. When noise or other erroneous features are present in thesignal with similar spectral characteristics of the features of band Bthen the information within band B can become ambiguous (i.e., obscured,fragmented or missing). In this case, the ridge of band A may befollowed in wavelet space and extracted either as an amplitude signal ora scale signal which will be referred to as the “ridge amplitudeperturbation” (RAP) signal and the “ridge scale perturbation” (RSP)signal, respectively. The RAP and RSP signals may be extracted byprojecting the ridge onto the time-amplitude or time-scale planes,respectively. The top plots of FIG. 3( d) show a schematic of the RAPand RSP signals associated with ridge A in FIG. 3( c). Below these RAPand RSP signals are schematics of a further wavelet decomposition ofthese newly derived signals. This secondary wavelet decomposition allowsfor information in the region of band B in FIG. 3( c) to be madeavailable as band C and band D. The ridges of bands C and D may serve asinstantaneous time-scale characteristic measures of the signalcomponents causing bands C and D. This technique, which will be referredto herein as secondary wavelet feature decoupling (SWFD), may allowinformation concerning the nature of the signal components associatedwith the underlying physical process causing the primary band B (FIG. 3(c)) to be extracted when band B itself is obscured in the presence ofnoise or other erroneous signal features.

In some instances, an inverse continuous wavelet transform may bedesired, such as when modifications to a scalogram (or modifications tothe coefficients of a transformed signal) have been made in order to,for example, remove artifacts. In one embodiment, there is an inversecontinuous wavelet transform which allows the original signal to berecovered from its wavelet transform by integrating over all scales andlocations, a and b:

$\begin{matrix}{{x(t)} = {\frac{1}{C_{g}}{\int_{- \infty}^{\infty}{\int_{0}^{\infty}{{T\left( {a,b} \right)}\frac{1}{\sqrt{a}}{\psi\left( \frac{t - b}{a} \right)}\frac{\ {{\mathbb{d}a}\ {\mathbb{d}b}}}{a^{2}}}}}}} & (15)\end{matrix}$which may also be written as:

$\begin{matrix}{{x(t)} = {\frac{1}{C_{g}}{\int_{- \infty}^{\infty}{\int_{0}^{\infty}{{T\left( {a,b} \right)}{\psi_{a,b}(t)}\frac{\ {{\mathbb{d}a}\ {\mathbb{d}b}}}{a^{2}}}}}}} & (16)\end{matrix}$where C_(g) is a scalar value known as the admissibility constant. It iswavelet type dependent and may be calculated from:

$\begin{matrix}{C_{g} = {\int_{0}^{\infty}{\frac{\left| {\hat{\psi}(f)} \right|^{2}}{f}\ {\mathbb{d}f}}}} & (17)\end{matrix}$FIG. 3( e) is a flow chart of illustrative steps that may be taken toperform an inverse continuous wavelet transform in accordance with theabove discussion. An approximation to the inverse transform may be madeby considering equation (15) to be a series of convolutions acrossscales. It shall be understood that there is no complex conjugate here,unlike for the cross correlations of the forward transform. As well asintegrating over all of a and b for each time t, this equation may alsotake advantage of the convolution theorem which allows the inversewavelet transform to be executed using a series of multiplications. FIG.3( f) is a flow chart of illustrative steps that may be taken to performan approximation of an inverse continuous wavelet transform. It will beunderstood that any other suitable technique for performing an inversecontinuous wavelet transform may be used in accordance with the presentdisclosure.

FIG. 4 is an illustrative continuous wavelet processing system inaccordance with an embodiment. In an embodiment, input signal generator410 generates an input signal 416. As illustrated, input signalgenerator 410 may include oximeter 420 coupled to sensor 418, which mayprovide as input signal 416, a PPG signal. It will be understood thatinput signal generator 410 may include any suitable signal source,signal generating data, signal generating equipment, or any combinationthereof to produce signal 416. Signal 416 may be any suitable signal orsignals, such as, for example, biosignals (e.g., electrocardiogram,electroencephalogram, electrogastrogram, electromyogram, heart ratesignals, pathological sounds, ultrasound, or any other suitablebiosignal), dynamic signals, non-destructive testing signals, conditionmonitoring signals, fluid signals, geophysical signals, astronomicalsignals, electrical signals, financial signals including financialindices, sound and speech signals, chemical signals, meteorologicalsignals including climate signals, and/or any other suitable signal,and/or any combination thereof.

In an embodiment, signal 416 may be coupled to processor 412. Processor412 may be any suitable software, firmware, and/or hardware, and/orcombinations thereof for processing signal 416. For example, processor412 may include one or more hardware processors (e.g., integratedcircuits), one or more software modules, computer-readable media such asmemory, firmware, or any combination thereof. Processor 412 may, forexample, be a computer or may be one or more chips (i.e., integratedcircuits). Processor 412 may perform the calculations associated withthe continuous wavelet transforms of the present disclosure as well asthe calculations associated with any suitable interrogations of thetransforms. Processor 412 may perform any suitable signal processing ofsignal 416 to filter signal 416, such as any suitable band-passfiltering, adaptive filtering, closed-loop filtering, and/or any othersuitable filtering, and/or any combination thereof.

Processor 412 may be coupled to one or more memory devices (not shown)or incorporate one or more memory devices such as any suitable volatilememory device (e.g., RAM, registers, etc.), non-volatile memory device(e.g., ROM, EPROM, magnetic storage device, optical storage device,flash memory, etc.), or both. The memory may be used by processor 412to, for example, store data corresponding to a continuous wavelettransform of input signal 416, such as data representing a scalogram. Inone embodiment, data representing a scalogram may be stored in RAM ormemory internal to processor 412 as any suitable three-dimensional datastructure such as a three-dimensional array that represents thescalogram as energy levels in a time-scale plane. Any other suitabledata structure may be used to store data representing a scalogram.

Processor 412 may be coupled to output 414. Output 414 may be anysuitable output device such as, for example, one or more medical devices(e.g., a medical monitor that displays various physiological parameters,a medical alarm, or any other suitable medical device that eitherdisplays physiological parameters or uses the output of processor 412 asan input), one or more display devices (e.g., monitor, PDA, mobilephone, any other suitable display device, or any combination thereof),one or more audio devices, one or more memory devices (e.g., hard diskdrive, flash memory, RAM, optical disk, any other suitable memorydevice, or any combination thereof), one or more printing devices, anyother suitable output device, or any combination thereof.

It will be understood that system 400 may be incorporated into system 10(FIGS. 1 and 2) in which, for example, input signal generator 410 may beimplemented as parts of sensor 12 and monitor 14 and processor 412 maybe implemented as part of monitor 14.

FIG. 5 shows an illustrative plot of a PPG signal 510 taken during aperiod of decreasing signal quality in accordance with an embodiment.Plot 500 displays time on the x-axis and light intensity on the y-axis.The y-axis may represent the light intensity detected by detector 18(FIG. 1) that emanates from the tissue of patient 40 (FIG. 1). Largervalues on the y-axis indicate larger light intensity measurements thansmaller values on the y-axis (for example, light intensity value 502represents a larger light intensity than light intensity value 504). PPGsignal 510 may be obtained, for example, from sensor 12 (FIG. 1) or fromaveraging or otherwise combining a plurality of signals derived from asuitable sensor array, as discussed in relation to FIG. 1. Plot 500 maybe displayed using any suitable display device such as, for example,monitor 20 (FIG. 1), display 28 (FIG. 1), a PDA, a mobile phone, or anyother suitable display device. Additionally, plot 500 may be displayedon multiple display devices, or it may not be displayed on any displaydevices.

A period of decreasing signal quality is a period in which the “quality”of the PPG signal 510 decreases in some way. The quality of PPG signal510 may be determined using a system such as system 10 (FIGS. 1 and 2)and/or system 400 (FIG. 4) to perform a suitable analysis of the signal.For example, the quality of PPG signal 510 may be characterized byanalyzing the energy of the signal or by calculating the signal-to-noiselevel of the signal. These characteristics may be calculated, forexample, from a suitable scalogram of PPG signal 510, and in particular,may be calculated using one or more portions of such a scalogram.

A period of decreasing signal quality may indicate that the intendedtarget stimulus (e.g., patient's 40 (FIG. 2) fingertip, toe, forehead,earlobe, or foot) is not being adequately measured by, for example,pulse oximetry system 10 (FIGS. 1 and 2). Possible causes of a period ofdecreasing signal quality may include: sensor 20 (FIG. 1) being slowlydislodged from patient 40 (FIG. 2), sensor 20 (FIG. 1) or anyconstituent component of the sensor 20 (FIG. 1) being damaged orotherwise malfunctioning, and/or a connecting cable (e.g., cable 24, 32,or 34 of FIG. 1) being gradually removed or otherwise malfunctioning.For example, PPG signal 510 was generated during an experiment in whichthe pulse oximeter probe was gradually loosened from the finger ofpatient 40 (FIG. 2). A related example of a PPG signal for which thereis a period of decreased signal quality will be shown in FIG. 9.

Plot 500 may contain several characteristics that may be used eitherindividually or in combination to identify a period of decreasing signalquality. Plot 500 is comprised of time periods 520, 530, and 540. Timeperiod 520 may correspond to a time period before a period of decreasingsignal quality or it may correspond to a period in which a decreasingsignal quality is largely imperceptible in PPG signal 510. In timeperiod 520, PPG signal 510 has a relatively small light intensityamplitude and exhibits relatively large oscillations in the lightintensity amplitude. A small light intensity amplitude may mean that areduced amount of light is measured at detector 18 (FIG. 1), which mayindicate that the target stimulus is being properly measured. Similarly,large oscillations in the amplitude may indicate the presence of strongpulse signal from patient 40 (FIG. 2), which may also indicate that thetarget stimulus is being properly measured.

In time period 530, PPG signal 510 exhibits a generally increasing lightintensity amplitude and smaller oscillations in the light intensityamplitude compared to those exhibited in time period 520. These featuresmay indicate that the signal quality of PPG signal 510 is decreasing.Such a decreasing trend in the signal quality of PPG signal 510 may becaused by any of a variety of factors, such as those discussed above. Inaddition to the these general trends, PPG signal 510 may exhibitscattered spurious effects that are characterized by rapid, and possiblytemporary, changes in the light intensity amplitude. Examples includeamplitude increase 550 and/or amplitude fluctuation 560. Amplitudeincrease 550 may correspond to, for example, a rapid and partialloosening of the pulse oximeter probe on patient 40 which causes morelight to reach detector 18 (FIG. 1) from emitter 16 (FIG. 1). Amplitudefluctuation 560 may correspond to, for example, a temporary tighteningand then re-loosening of the pulse oximeter probe on the target stimuluson patient 40 (FIG. 2), which temporarily decreases the amount of lightreceived by detector 18 (FIG. 1).

Time period 540 may correspond to, for example, the case where the pulseoximeter probe is nearly or completely removed from the target stimuluson patent 40 (FIG. 2). In time period 540, PPG signal 510 reaches anapproximately constant light intensity amplitude value 506. Also, PPGsignal 510 exhibits only small oscillations in light intensity amplitudeduring this period. These characteristics may indicate that the intendedtarget stimulus on patient 40 (FIG. 2) is no longer being measured to asignificant degree.

As mentioned above, plot 500 was generated during an experiment in whichthe pulse oximeter probe was gradually loosened from the finger ofpatient 40 (FIG. 2) over time. However, as emphasized above, plot 500 ismerely illustrative of a general PPG signal that may be obtained from,for example, pulse oximetry system 10 (FIG. 1) or system 400 (FIG. 4).Further, and as emphasized above, the target stimulus need notcorrespond to a patient finger, as many other target stimuli wouldproduce a plot similar to plot 500. The actual rate of light intensityamplitude decrease may be faster or more gradual than that shown in timeperiod 530, and the nature and number of energy increases and energyfluctuations in PPG signal 510 may be unpredictable and variable duringtime period 520.

FIG. 6 shows an illustrative scalogram 600 of a wavelet transformderived from a PPG signal such as PPG signal 510 (FIG. 5) during aperiod of decreasing signal quality in accordance with an embodiment. Inscalogram 600, the x-axis of denotes time and the y-axis denotes scale.In scalogram 600, “hotter” colors (e.g., hues of red, orange and yellow)correspond to larger energy values, while “cooler” colors (e.g., hues ofblue and green) correspond to smaller energy values. Dark red, which isthe color of region 680, represents the largest energy value inscalogram 600, whereas dark blue, which is the color of region 690,represents the smallest energy values in scalogram 600. The regions inthe lower left and lower right corners of the plot may contain energyvalues that reflect edge effects of the wavelet transform. These regionsmay be of a very high energy, and in scalogram 600 these regions havebeen replaced by energy values corresponding to the lowest energy valuespresent in scalogram 600. This has been done so that energy values inthese regions do not adversely influence the calculation of color scaleused to generate scalogram 600. These regions may be ignored in theanalysis of the scalogram.

Scalogram 600 comprises at least three distinct scale bands (i.e.,ranges of scale values): pulse band 610, mains hum band 620, and noiseband 630. Pulse band 610 may contain an energy structure and energyvalues that reflect the pulse component of PPG signal 510 (FIG. 5). Whena pulse component is present in PPG signal 510 (FIG. 5) (e.g., when anaccurate measurement of a target stimulus is made), pulse band 610 maybe characterized by moderate to high energy values within the band andareas of lower energy at surrounding scale values. When a pulsecomponent is not present in PPG signal 510 (FIG. 5) (e.g., when anaccurate measurement of the target stimulus is not made), it may beexpected that pulse band 610 will contain less energy. Mains hum band620 may contain an energy structure and energy values that reflect theelectrical or power line hum that is often characteristic of electricdevices. The scales range that defines the mains hum band 620 may dependon characteristics of the alternating current supply (e.g., as used bypulse oximetry system 10 (FIG. 1)). When such a mains hum component ispresent in PPG signal 510 (FIG. 5), mains hum band 610 may becharacterized by regular and rapidly oscillating streaks of low tomoderate energy and areas of lower energy at surrounding scales. Noiseband 630 may contain an energy structure and energy values that reflectgeneral types of noise that may be present in PPG signal 510 (FIG. 5).For example, noise band 630 may be include the effects of thermal noise,shot noise, flicker noise, burst noise, and/or electrical noise causedby light pollution. Noise band 610 may be characterized as having lessenergy structure than either pulse band 610 or mains hum band 620, andas containing low-to-moderate energy values.

In scalogram 600, time periods 640, 650, and 660 correspond to the timeperiods 520, 530, and 540, respectively, as discussed in FIG. 5.Therefore, time period 640 corresponds to the time period for which theeffect of the target stimulus is nearly or fully captured in themeasurement of PPG signal 510 (FIG. 5). Time period 630 corresponds to atime period of decreasing signal quality, and time period 660corresponds to the time period after which the effect of the targetstimulus is largely or completely uncaptured in measurement of PPGsignal 510 (FIG. 5). Within pulse band 610, energy values are seen to bemoderate in time period 640, decreasing from moderate to low in timeperiod 650, and low in time period 660. This may reflect the diminishingpresence of a measurable pulse component signal in PPG signal 510 (FIG.5) during time period 650. In contrast, the energy in the mains hum band620 remains approximately constant throughout time periods 640, 650, and660. This is because the mains hum noise is generated by electricalcircuitry and may not depend on the signal that is measured (e.g., bydetector 18 (FIG. 1)). The energy in noise band 630 decreases frommoderate and low energy values in time period 640 to very low energyvalues in time period 660. This is because certain components of PPGsignal 510 (FIG. 5) are contained within the scales comprising the noiseband 630. Therefore, as the quality of the PPG signal 510 (FIG. 5)decreases, these components are not measured by, for example, detector18 (FIG. 1), which results in less energy being detected in noise band630.

Broadscale high-energy cone 670 is a sporadic effect caused by energyfluctuation 560 (FIG. 5) and is characterized by a cone-shaped region ofhigh-energy that has a width that decreases as the scale valueincreases. The location and number of broadscale high-energy cones is ingeneral variable and may be unpredictable in advance. However, thepresence of one or more broadscale high-energy cones in a time periodsuch as time period 650 may be indicative a signal quality decrease inthat time period.

FIG. 7 shows illustrative plots of an energy measure versus time derivedfrom scalogram 600 (FIG. 6). Each plot in FIG. 7 has been calculated bytaking the tenth-percentile of the energy density values in scalogram600 (FIG. 6) over a 10-second long time-window that includes a certainrange of scale values. For example, plot 710 has been generated byselecting scale values in pulse band 610 (FIG. 6), plot 720 has beengenerated by selecting scale values in the mains hum band 620 (FIG. 6),and plot 730 has been generated by selecting scale values in the noiseband 630 (FIG. 6). The energy measure plotted in FIG. 7 has been plottedusing a logarithmic scale on the y-axis. It should be noted that plot710 has the largest amplitudes followed by plot 720 and then plot 730.This is expected because, as discussed in relation to scalogram 600(FIG. 6), pulse band 610 (FIG. 6) has the largest energy, followed bymains hum band 620 (FIG. 6) and noise band 630 (FIG. 6). Plot 710exhibits a decrease in the energy measure in the pulse band versus time(e.g., plot 710 has a value of approximately 4 at time 10, and decreasesto a value of approximately 2.25 at time 180) and plot 720 shows arelatively constant energy value in time (e.g., plot 720 has a value ofapproximately 2.1 at time 10, and a value of approximately 1.9 at time180). These results are expected because, as discussed in relation toscalogram 600 (FIG. 6), pulse band 610 (FIG. 6) decreases in energyversus time, while mains hum band 620 (FIG. 6) has an approximatelyconstant energy versus time. Plot 730 shows a small to moderate energydecrease in energy versus time (e.g., plot 730 has a value ofapproximately 0 at time 10, and a value of approximately −0.6 at time180). In general, the level of decrease in plot 730 depends, at least inpart, on the percentile threshold chosen in to generate plot 730. Theenergy used above is merely illustrative. Alternative energy measures(i.e., other than taking the tenth-percentile value in the window) canbe employed. For example, summing all of the values of the lowesttenth-percentile may also be used.

In one embodiment, plots 710, 720, and/or 730 may be monitored and/orcombined, and used to determine when and if a signal quality decreasehas occurred or if one may occur. For example, plots 710, 720, and 730may be parameterized through, for example, curve fitting using a linearstraight line fit or a nonlinear curve fit. Alternatively, or incombination, plots 710, 720, and 730 may be combined through anysuitable operation that, for example, weighs the values present in eachplot or in some subset of plots. This weighted data may be compared to athreshold to determine if a signal quality decrease has occurred.Further, many changes to the parameters and features used to generateplots such as the ones illustrated in FIG. 7 may be made in accordancewith an embodiment. For example, in choosing regions of the scalogram600 (FIG. 6) over which to sum the energy density, the length of thetime-window may be shortened or lengthened and may be chosen to includenon-continuous segments; the selection of scale values used to computeeach plot may be altered (e.g., to use more or fewer scale values);and/or a different threshold percentile (i.e., other than 10-percent)may be chosen in computing plot 730. Alternatively, instead of computingthe tenth-percentile of energy density, plots may be generated byanother energy measure. For example, plots may be generated in which theenergy measure computes the lowest fifth-percentile,twentieth-percentile, or any other suitable percentile of energy values.Further, other properties of the wavelet transform can be use togenerate plots other than or in addition to those of 710, 720, and 730.For example, the real and/or imaginary components, and various powers ofthe modulus and phase may be used.

FIG. 8 shows illustrative plots of signal-to-noise levels versus timederived from FIG. 7. Plots 810 and 820 represent an illustrativetechnique for weighing the information in plots 710, 720, and 730 fromFIG. 7 in accordance with an embodiment. Plot 810 is plot of the ratioof pulse band 610 (FIG. 6) energy to the noise band 620 (FIG. 6) energyand is obtained by dividing, at each point in time, the value of plot710 (FIG. 7) by the value of plot 730 (FIG. 7). Plot 820 is a plot ofthe ratio of the pulse band 610 (FIG. 6) energy to the mains hum band620 (FIG. 6) energy and is obtained by dividing, at each point in time,the value of plot 710 (FIG. 7) by the value of plot 720 (FIG. 7).Signal-to-noise level plots such as 810 and 820 may be useful at leastbecause they provide a measure of how the signal energy changes relativeto a background noise level. For example, a large decrease in the signalenergy might be tolerable, and not indicative of a signal qualitydecrease, if accompanied by a correspondingly large decrease in thenoise level. In one embodiment, plots 810 and/or 820 may be combined andused to determine when a signal quality decrease occurs. For example,plots 810 and 820 may be combined through any suitable operation thatweighs the values present in each plot to generate a new plot, which maybe used to determine if a signal quality decrease has occurred. Plots810 and 820 each have a signal decrease of roughly 1.5 from time 10 totime 180. In one embodiment, a signal quality decrease may be detectedby comparing the average decrease from time 10 to time 180 between plots810 and 820 to a threshold. If the average decrease exceeds thisthreshold value, then a signal quality decrease may be said to occur.The threshold may be calculated using any suitable technique. In oneembodiment, such a threshold may be determined using statisticaltechniques such Neyman-Pearson hypothesis testing or themaximum-likelihood detection. Alternatively, such a threshold may bedetermined using historical data on signal-to-noise levels calculatedbefore and during a signal quality decrease event. Alternatively or incombination, plots 810 and 820 may be parameterized through, forexample, curve fitting using a linear straight line fit or a nonlinearcurve fit before being used to determine whether a signal qualitydecrease has occurred.

FIG. 9 shows an illustrative plot of a PPG signal 910 taken prior to andduring a period which includes a motion artifact in accordance anembodiment. As will be explained below, the occurrence of a motionartifact may result in a signal quality decrease in PPG signal 910. Thedefinitions and meanings of the x-axis and y-axis in plot 900 are thesame as for plot 500 (FIG. 5).

Plot 900 includes time periods 920 and 930. Time period 920 correspondsto a time period before a time period in which a motion artifact ispresent in PPG signal 910. Starting at approximately time 930, asignificant motion artifact is measured in PPG signal 910. Such a motionartifact may be caused by, for example, voluntary or involuntaryrespiration, eye movements, swallowing, yawning, cardiac motion, and/orgeneral body movement of patient 40. At approximately time 930, a motionartifact occurs in PPG signal 910. Time period 940 corresponds to aperiod in which a motion artifact remains present and measured by, forexample, sensor 12. The presence of the motion artifact leads to adistinct change in PPG signal 910 during time period 940. The PPG signalexhibits larger and less smooth oscillations in light intensityamplitude during time period 940 than during time period 920. Further,the light intensity amplitudes exhibited in time period 940 aregenerally smaller than those exhibited in time period 920. Thesefeatures may be used either singly or in combination to identify aperiod of decreased signal quality caused by a significant motionartifact or another related phenomena in PPG signal 910.

FIG. 10 shows an illustrative scalogram derived from the PPG signal 910(FIG. 9) in accordance with an embodiment. The axes values and themeanings of the colors in scalogram 1000 are the same as for scalogram600 (FIG. 6). In scalogram 1000, region 1010 (having dark blue and lightblue colors) and the lower-left and lower-right portions of the plot(each having a dark blue color) are the regions of the smallest energy,whereas scale band 1020 and region 1030 (having mostly dark red colors)are the regions of the largest energy, and region 1040 (having mostlyyellow and orange colors) is a region of moderate energy. In scalogram1000, time periods 1050 and 1060 correspond to the time periods 920 and940, respectively, discussed above with respect to FIG. 9. Therefore,time period 1050 corresponds to a time period before the appearance andmeasurement of a motion artifact, and time period 1060 corresponds toperiod in which the motion artifact is present and measured. The motionartifact is first measured at time 1070 which corresponds to time 930(FIG. 9). Scalogram 1000 exhibits different characteristics in timeperiod 1050 than in time period 1060. The energy structure of scalogram1000 is more random and the energy amplitudes of scalogram 1000 arelarger in time period 1050 than in time period 1060. For example, therange of scales below scale band 1020 contain mostly low energy valuesin time period 1050 but mostly moderate and high energy values in time1060. The range of scales immediately above scale band 1020 containstructured and repeated energy characteristics in time period 1050 butrelatively less well-defined energy characteristics in time period 1060.These features can be used to detect the presence of a signal qualitydecrease due to a motion artifact or other related phenomena.

FIG. 11 is a flow chart 1100 of illustrative steps for determining andresponding to a decrease in signal quality in accordance with anembodiment. Flow chart 1100 may begin at step 1102. At step 1104, aportion of a suitable signal may be obtained using, for example, pulseoximetry system 10 (FIGS. 1 and 2) or system 400 (FIG. 4). The signalmay be obtained from a target stimulus provided by patient 40 (FIG. 2).The signal obtained may be a PPG signal. At step 1106, the wavelettransform of the signal may be obtained. Such a wavelet transform may beobtained, for example, by system 10 (FIGS. 1 and 2) or system 400 (FIG.4). At step 1108, the scalogram of the wavelet transform may begenerated or otherwise obtained using, for example a processor. Forexample, the scalogram of the wavelet transform may be generated orobtained using a processor such as processor 412 (FIG. 4) ormicroprocessor 48 (FIG. 2).

In addition to the scalogram, other parts of the wavelet transform maybe inspected to determine whether a signal quality decrease event hasoccurred. For example, the transform modulus, phase, real, and/orimaginary parts may be generated at step 1108 in place of or in additionto the scalogram. Each of these features may then be used, eitherindividually or in combination, in the subsequent steps of flow chart1100. For example, the transform modulus or real, and/or imaginaryvalues corresponding to the wavelet transform may be summed or otherwisemanipulated to generate plots that can be used along with, or insteadof, the plots shown in FIG. 7 and FIG. 8 to determine whether a signalquality decrease event has occurred. Alternatively or in addition to themethod describe above, the phase of a wavelet transform may be analyzedacross a range of scale values. The stability of phase values mayindicate a relative value of the signal quality, and may be used todetermine whether a signal quality decrease event has occurred.

Referring back to FIG. 11, at step 1110, the scalogram obtained in step1108 may be quantized. Quantization refers to a process of truncating acontinuous or (high-precision) digital signal value to a nearestreference value. The number of reference values may be significantlysmaller than the number of values present in the signal prior toquantization. Quantization may be beneficial at least for decreasing thecomplexity of hardware and software resources required to process andstore the scalogram obtained in step 1108, as well as to further aid inthe detection and analysis of features of the scalogram in subsequentsteps 1112 and 1114. Quantization may provide these benefits with only asmall or imperceptible degradation in the quality of the quantizedsignal relative to the quality of the signal prior to quantization. Anysuitable quantization scheme may be used in step 1110. For example,quantization of the scalogram obtained in step 1108 may be performedusing one, two, or multiple thresholds, whereby the quantized scalogramis obtained by rounding energy values of the original scalogram to thenearest threshold value. The threshold value may be calculated using anysuitable technique. In one embodiment, such a threshold may bedetermined using statistical techniques such Neyman-Pearson hypothesistesting or the maximum-likelihood detection. Alternatively, such athreshold may be determined using historical data on signal-to-noiselevels calculated before and during a signal quality decrease event. Inaddition, the number and value of quantization levels may be chosenbased on the dynamic range of scalogram obtained in step 1108, thecomputational resources available, or based on a combination of theseand any other suitable factors. Each threshold may be a variablequantity that varies with, for example, the time or scale value. It willbe understood that step 1110, as well all other steps of flow chart1100, is optional and that quantization of the scalogram need not beperformed.

Referring back to FIG. 11, at step 1112, one or more characteristics ofthe scalogram obtained in step 1108 or 1110 may be determined using aprocessor. One or more of the characteristics that is determined may bechosen to be beneficial in determining if a signal quality decrease hasoccurred. For example, characteristics that may be determined includethe energy and structure of the scalogram in pulse band 610 (FIG. 6),mains hum band 620 (FIG. 6), and/or noise band 630 (FIG. 6), and thesignal-to-noise levels in various regions of scalogram 600 (FIG. 6). Inone embodiment, this information may be calculated one or more timesusing different time-window sizes. The number and type of time-windowsizes that are used may depend on the anticipated rate of a possiblesignal quality decrease, the available computational resources (e.g.,the amount of ROM 52 (FIG. 2) and/or RAM 54 (FIG. 2) and the speed ofprocessor 412 (FIG. 4) and/or microprocessor 48 (FIG. 2)), as well as onpossible input derived from user inputs 56 (FIG. 2).

Referring back to FIG. 11, at step 1114, the characteristics determinedin step 1112 may be analyzed. Analyzing the characteristics maygenerally involve parsing, combining, and/or weighing individual resultsobtained in the current and possible previous iterations of step 1114 sothat a single, overall decision may be made as to whether a signalquality decrease has occurred. Step 1114 may incorporate the use of pastscalogram data that has been obtained in previous iterations of process1100 to determine current signal quality values and also trends in thesignal quality values. For example, a signal quality value may berepresented by a number from 0 to 100, where a larger number indicates ahigher quality signal, and a trend may be represented by a numberrepresenting a rate increase or decrease in the signal quality. Pastscalogram data may be stored in, for example, ROM 52 (FIG. 2) and/or RAM54 (FIG. 2). Step 1114 may also involve the parameterization and/orcurve fitting of data obtained in step 1112 using, for example, linearleast-squares fitting of data or any other suitable interpolationtechnique. Such parameterization and/or curve fitting may be performed,for example, by processor 412 (FIG. 4) or microprocessor 48 (FIG. 2),and may additionally depend on parameters entered by a user through userinputs 56 (FIG. 2). In step 1114, multiple signal quality values andtrend data may be combined to produce a simple form of data that may beused to make a single overall decision as to the possible presence of asignal quality decrease in a PPG signal such as PPG signal 510 (FIG. 5)or PPG signal 910 (FIG. 9). Any suitable parsing, combining, and/orweighing strategy may be used. For example, maximum-likelihoodtechniques may be used to combine data when the prior probability of asignal decrease event is known, and Neyman-Pearson combining techniquesmay be used when the prior probability of a signal quality decreaseevent is unknown. In addition, simply majority-vote decision rules maybe used to determine if a signal quality decrease has occurred.

Referring back to FIG. 11, at step 1116, a decision may be made as towhether a decrease in signal quality has occurred. Such a decision maybe made based on the output of step 1114. If it is determined that adecrease in signal quality has occurred, a response may be initiated instep 1118. A response may include many features singly or incombination. For example, possible features may include generating anaudible alert or alarm that is emitted, for example, using speaker 22(FIG. 2) as well as possibly through other audio devices, generating anon-screen message, for example, on display 20 (FIG. 1) or display 28(FIG. 1), generating a pager message, a text message, or a telephonecall, for example, using a wireless connection embedded or attached to asystem such as system 10 (FIG. 1), activating a secondary or backupsensor or sensor array, for example, connected through a wire orwirelessly to monitor 14 (FIG. 1), or regulating the automaticadministration medicine, for example, which is controlled in part orfully through a system such as system 10 (FIG. 1). If it is determinedthat a signal quality decrease has not occurred, then process 1100returns to step 1104 and the “next” portion of the signal is obtained.The next portion of the signal may start where the previously readsignal ended, overlap with the previously read signal, or be located atsome distance in the future from the previously read signal. In any ofthese or in other scenarios, the choice of the signal region to beselected may be influenced by the data determined in step 1112 oranalyzed in step 1114.

FIG. 12 shows a flow chart of illustrative steps for performing step1112 of FIG. 11 (i.e., for determining a set of characteristicscorresponding to a scalogram) in accordance with an embodiment. At step1210, one or more time-windows may be determined and used to calculatethe energy and other characteristics of the scalogram determined in step1108 or step 1110. For example, and as described previously in relationto FIG. 7, the energy values and structural characteristics in the pulseband 610 (FIG. 6), mains hum band 620 (FIG. 6), and noise band 630 (FIG.6) may be calculated. A separate time-window may be determined for eachof these scale bands as well as other scale bands, and separatetime-windows may be determined to measure energy values and energystructural characteristics. Time-windows may be of the same or differentlengths, and each time-window may be comprised of either continuous ordiscontinuous ranges of time.

Referring back to FIG. 12, at step 1220, the energy values and energystructure in pulse band 610 (FIG. 6) may be calculated, for example,using one or more time-windows determined in step 1210. Energy valuesmay be calculated by averaging the energy densities of a scalogram suchas scalogram 600 (FIG. 6) within a given time-window as described inrelation to FIG. 7, or through any other suitable technique. Forexample, energy may be calculated by averaging the energy density onlyover those energy values below a certain percentile threshold in pulseband 610 (FIG. 6), or by averaging only the minimum or maximum values ateach time instant. Alternatively, the energy structure in pulse band 610(FIG. 6) may be calculated by recording the presence of features withina given time-window such as the number of and frequency of repeatedpatterns, the presence of high energy regions followed by low energyregions, and/or any other suitable characteristics.

Referring back to FIG. 12, at step 1230, the energy values and energystructure in mains hum band 620 (FIG. 6) may be calculated, for example,using one or more time-windows determined in step 1210. Energy valuesmay be calculated by averaging the energy densities of a scalogram suchas scalogram 600 (FIG. 6) within a given time-window as described inrelation to FIG. 7, or through any other suitable technique. Forexample, energy may be calculated by averaging the energy density onlyover energy values below a certain percentile threshold in mains humband 620 (FIG. 6), or by averaging only the minimum or maximum values ateach time instant. Alternatively, the energy structure in mains hum band620 (FIG. 6) may be calculated by recording the presence of featureswithin a given time-window such as the number of and frequency ofrepeated patterns, the presence of high energy regions followed by lowenergy regions, and/or any other suitable characteristics.

Referring back to FIG. 12, at step 1240, the energy values and energystructure in noise band 630 (FIG. 6) may be calculated, for example,using one or more time-windows determined in step 1210. Energy valuesmay be calculated by averaging the energy densities of a scalogram suchas scalogram 600 (FIG. 6) within a given time-window as described inrelation to FIG. 7, and/or through any other suitable scheme. Forexample, energy may be calculated by averaging the energy density onlyover energy values below a certain percentile threshold in noise band630 (FIG. 6), or by averaging only the minimum or maximum values at eachtime instant. Alternatively, the energy structure in noise band 630(FIG. 6), may be calculated by recording the presence of features withina given time-window such as the number of and frequency of repeatedpatterns, the presence of high energy regions followed by low energyregions, and/or any other suitable characteristics.

Referring back to FIG. 12, at step 1250, signal-to-noise levels may becalculated corresponding to the characteristics determined in steps1220, 1230, and 1240 above. One or more signal-to-noise levels may becalculated as described in relation to FIG. 8 or through any othersuitable scheme. For example, the signal-to-noise level between thepulse band 610 (FIG. 6) and mains hum band 620 (FIG. 6) may becalculated by dividing, at each time point, the energy value obtained instep 1220 by the energy value obtained in step 1230. Alternatively, thesignal-to-noise level between the pulse band 610 (FIG. 6) and noise band630 (FIG. 6) may be calculated by dividing, at each time point, theenergy value obtained in step 1220 by the energy value obtained in step1240. At step 1260, the characteristics determined in steps 1220, 1230,1240, and 1250 may be sent to step 1114 of process 1100 (FIG. 11).

FIG. 13 is a flow chart of illustrative steps for performing step 1114of FIG. 11, (i.e., analyzing the set of characteristics determined instep 1112) in accordance with an embodiment. At step 1310, curve fittingmay be performed on the plots of energy values, energy structuralcharacteristics, signal-to-noise levels, as well as on any other plotsor other data types that may have been determined in step 1112. Thecurve fitting may be done using linear least-squares fitting of data,higher-order interpolative methods, or any other suitable technique.Parameterization and/or curve fitting can be performed, for example, byprocessor 412 (FIG. 4) or microprocessor 48 (FIG. 2), and mayadditionally depend on parameters entered by a user through user inputs56 (FIG. 2). At step 1320, signal quality values and associated signalquality trends may be calculated based on current data obtained fromstep 1310 as well as from past data (generated in previous iterations ofprocess 1100 (FIG. 11)) stored in step 1330. Such past data may bestored in, for example, ROM 52 (FIG. 2) and/or RAM 54 (FIG. 2). At step1320, a separate signal quality value and related signal quality trendmay be obtained for each type of available data. For example, a separatesignal quality value and associated signal quality trend may bedetermined from each of the signal-to-noise level plots 810 (FIG. 8) and820 (FIG. 8). A signal quality value may be represented by a number from0 to 100, where a larger number indicates a higher quality signal andmay be determined, for example, using tabulated figures or merit orthrough any other suitable scheme. In addition, signal quality trendsmay be determined and stored. A signal quality trend may be determinedby comparing data obtained from step 1310 with past data obtained fromstep 1330. A trend may be characterized by a number representing a rateof increase or rate of decrease in the signal quality, or by anotherother suitable statistic. At step 1340, the multiple signal qualityvalues and signal quality trends determined in step 1320 may be combinedto produce a single signal quality value and associated signal qualitytrend. Any suitable parsing, combining, or weighing strategy may beused. For example, maximum-likelihood techniques may be used to combinedata when the prior probability of a signal quality decrease event isknown, and Neyman-Pearson combining techniques may be used when theprior probability of a signal quality decrease event is unknown. Theoverall signal quality value and associated signal quality trend may bepassed to step 1116 (FIG. 11).

It will also be understood that the above method may be implementedusing any human-readable or machine-readable instructions on anysuitable system or apparatus, such as those described herein.

The foregoing is merely illustrative of the principles of thisdisclosure and various modifications can be made by those skilled in theart without departing from the scope and spirit of the disclosure. Thefollowing claims may also describe various aspects of this disclosure.

What is claimed is:
 1. A method of determining signal qualityinformation of a physiological signal, comprising: obtaining thephysiological signal; generating a scalogram based at least in part on awavelet transform of the physiological signal; determining one or morefirst characteristics of a pulse band of the scalogram; determining oneor more second characteristics of a mains hum band or a noise band ofthe scalogram; and determining signal quality information of thephysiological signal based at least in part on the one or more firstcharacteristics and the one or more second characteristics.
 2. Themethod of claim 1, wherein the one or more first characteristicscomprise one or more of energy measures of the pulse band and energystructure of the pulse band.
 3. The method of claim 1, wherein the oneor more second characteristics comprise one or more of energy measuresof the mains hum band, energy measures of the noise band, energystructure of the mains hum band, and energy structure of the noise band.4. The method of claim 1, further comprising combining the one or morefirst characteristics and the one or more second characteristics.
 5. Themethod of claim 4, wherein combining comprises dividing one of the oneor more first characteristics and the one or more second characteristicsby the other of the one or more first characteristics and the one ormore second characteristics.
 6. The method of claim 1, whereindetermining the signal quality information comprises curve fitting theone or more first characteristics and the one or more secondcharacteristics.
 7. The method of claim 1, wherein determining thesignal quality information comprises: determining one or more signalqualities based at least in part on the one or more firstcharacteristics and the one or more second characteristics; determiningone or more signal quality trends based on the one or more signalqualities; and determining an overall signal quality based on the one ormore signal qualities and the one or more signal quality trends.
 8. Themethod of claim 1, wherein determining the signal quality informationcomprises determining a signal quality decrease event.
 9. The method ofclaim 1, wherein determining the signal quality information comprisesdetermining a signal-to-noise level.
 10. The method of claim 1, whereinthe physiological signal is a photoplethysmograph signal.
 11. A systemfor determining signal quality information of a physiological signal,comprising: a processor configured to: obtain the physiological signal;generate a scalogram based at least in part on a wavelet transform ofthe physiological signal; determine one or more first characteristics ofa pulse band of the scalogram; determine one or more secondcharacteristics of a mains hum band or a noise band of the scalogram;and determine signal quality information of the physiological signalbased at least in part on the one or more first characteristics and theone or more second characteristics.
 12. The system of claim 11, whereinthe one or more first characteristics comprise one or more of energymeasures of the pulse band and energy structure of the pulse band. 13.The system of claim 11, wherein the one or more second characteristicscomprise one or more of energy measures of the mains hum band, energymeasures of the noise band, energy structure of the mains hum band, andenergy structure of the noise band.
 14. The system of claim 11, whereinthe processor is further configured to combine the one or more firstcharacteristics and the one or more second characteristics.
 15. Thesystem of claim 14, wherein the processor is configured to combine theone or more first characteristics and the one or more secondcharacteristics by dividing one of the one or more first characteristicsand the one or more second characteristics by the other of the one ormore first characteristics and the one or more second characteristics.16. The system of claim 11, wherein the processor is configured todetermine the signal quality information by curve fitting the one ormore first characteristics and the one or more second characteristics.17. The system of claim 11, wherein the processor is configured todetermine the signal quality information by: determining one or moresignal qualities based at least in part on the one or more firstcharacteristics and the one or more second characteristics; determiningone or more signal quality trends based on the one or more signalqualities; and determining an overall signal quality based on the one ormore signal qualities and the one or more signal quality trends.
 18. Thesystem of claim 11, wherein the signal quality information comprises asignal quality decrease event.
 19. The system of claim 11, wherein thesignal quality information comprises a signal-to-noise level.
 20. Thesystem of claim 11, wherein the physiological signal is aphotoplethysmograph signal.